Unified long-range electrostatics and dynamic protonation for realistic biomolecular simulations on the Exascale

Unified long-range electrostatics and dynamic protonation for realistic biomolecular simulations on the Exascale

In this DFG supported project we target a flexible, portable and scalable solver for potentials and forces, which is a prerequisite for exascale applications in particle-based simulations with long-range interactions in general. As a particularly challenging example that will prove and demonstrate the capability of our concepts, we use the popular molecular dynamics (MD) simulation software GROMACS. MD simulation has become a crucial tool to the scientific community, especially as it probes time- and length scales difficult or impossible to probe experimentally. Moreover, it is a prototypic example of a general class of complex multiparticle systems with long-range interactions.

MD simulations elucidate detailed, time-resolved behaviour of biology’s nanomachines. From a computational point of view, they are extremely challenging for two main reasons. First, to properly describe the functional motions of biomolecules, the long-range effects of the electrostatic interactions must be explicitly accounted for. Therefore, techniques like the particle-mesh Ewald method were adopted, which, however, severely limits the scaling to a large number of cores due to global communication requirements. The second challenge is to realistically describe the time-dependent location of (partial) charges, as e.g. the protonation states of the molecules depend on their time-dependent electrostatic environment. Here we address both tighly interlinked challenges by the development, implementation, and optimization of a unified algorithm for long-range interactions that will account for realistic, dynamic protonation states and at the same time overcome current scaling limitations.

Download and test our GPU-FMM for GROMACS

If you want to give our GPU-FMM a test drive, please download the tar archive below, unpack with tar -xvzf, and install just like a usual GROMACS 2019.

Our CUDA FMM can be used as a PME replacement by choosing coulombtype = FMM in the .mdp input parameter list. The tree depth d and the multipole order p are set with fmm-override-tree-depth and fmm-override-multipole-order input parameters, respectively. On request (provide your ssh key), the code can be checked out from our git repository git@fmsolvr.fz-juelich.de:gromacs.

GROMACS with GPU-FMM including benchmark systems

For running the GPU FMM benchmarks, you need to set the following environment variable:


With sparse systems as the aerosol system, you should additionally set

export FMM_SPARSE=1

for optimum FMM performance.

Running FMM in standalone mode

You can also compile and run the GPU-FMM without GROMACS integration. The relevant code is in the ./src/gromacs/fmm/fmsolvr-gpu subdirectory of the above tar archive after unpacking. Compile it with a script like this:

; in bash
export CC=$( which gcc )
export CXX=$( which g++ )
cmake -H../git-gromacs-gmxbenchmarking/src/gromacs/fmm/fmsolvr-gpu -B. -DFMM_STANDALONE=1 -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.0

The python script runfmm.py can be used to benchmark the standalone version of the GPU-FMM.

Please follow this link to the external workshop website for more information. more
On May 19–20, a group of >50 GROMACS developers and users gathered at the Max Planck Institute for biophysical Chemistry in Göttingen to discuss various aspects of software development and future directions for GROMACS. more


Kohnke, B.; Kutzner, C.; Grubmüller, H.: A GPU-accelerated fast multipole method for GROMACS: Performance and accuracy. Journal of Chemical Theory and Computation 16 (11), pp. 6938 - 6949 (2020)
Kohnke, B.; Kutzner, C.; Beckmann, A.; Lube, G.; Kabadshow, I.; Dachsel, H.; Grubmüller, H.: A CUDA fast multipole method with highly efficient M2L farfield evaluationfield evaluation. The International Journal of High Performance Computing Applications 35 (1), pp. 97 - 117 (2021)
Kohnke, B.; Ullmann, R. T.; Beckman, A.; Kabadshow, I.; Haensel, D.; Morgenstern, L.; Dobrev, P.; Groenhof, G.; Kutzner, C.; Hess, B. et al.; Dachsel, H.; Grubmüller, H.: GROMEX: A scalable and versatile fast multipole method for biomolecular simulation. In: Software for Exascale Computing - SPPEXA 2016-2019, pp. 517 - 543 (Eds. Bungartz, H.-J.; Reiz, S.; Uekermann, B.; Neumann, P.; Nagel, W. E.). Springer, Cham (2020)
Kutzner, C.; Páll, S.; Fechner, M.; Esztermann, A.; de Groot, B. L.; Grubmüller, H.: More bang for your buck: Improved use of GPU nodes for GROMACS 2018. Journal of Computational Chemistry 40 (27), pp. 2418 - 2431 (2019)
Donnini, S.; Ullmann, T.; Groenhof, G.; Grubmüller, H.: Charge-neutral constant pH molecular dynamics simulations using a parsimonious proton buffer. Journal of Chemical Theory and Computation 12 (3), pp. 1040 - 1051 (2016)
Garcia, AG; Beckmann, A; Kabadshow, I
Accelerating an FMM-Based Coulomb Solver with GPUs
Lecture Notes in Computational Science and Engineering 113 (2016) "Software for Exascale Computing - SPPEXA 2013-2015", Eds. HJ Bungartz, P. Neumann, WE Nagel, Springer, pp. 485-504
Páll, S.; Abraham, M. J.; Kutzner, C.; Hess, B.; Lindahl, E.: Tackling exascale software challenges in molecular dynamics simulations with GROMACS. In: Solving Software Challenges for Exascale: International Conference on Exascale Applications and Software, EASC 2014, Stockholm, Sweden, April 2-3, 2014, Revised Selected Papers, pp. 3 - 27 (Eds. Markidis, S.; Laure, E.). Springer, Cham (2015)
A. Beckmann, I. Kabadshow
Portable Node-Level Performance Optimization for the Fast Multipole Method
Lecture Notes in Computational Science and Engineering 105, 29-46 (2015)
Kutzner, C.; Páll, S.; Fechner, M.; Esztermann, A.; de Groot, B.; Grubmüller, H.: Best bang for your buck: GPU nodes for GROMACS biomolecular simulations. Journal of Computational Chemistry 36 (26), pp. 1990 - 2008 (2015)
Kutzner, C.; Apostolov, R.; Hess, B.; Grubmüller, H.: Scaling of the GROMACS 4.6 molecular dynamics code on SuperMUC. In: Parallel Computing: Accelerating Computational Science and Engineering (CSE), pp. 722 - 730 (Eds. Bader, M.; Bode, A.; Bungartz, H. J.). IOS Press, Amsterdam (2014)
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Comparison of scalable fast methods for long-range interactions
Phys. Rev. E 88 063308-1-22 (2013)
R. T. Ullmann, G. M. Ullmann
GMCT: A Monte Carlo simulation package for macromolecular receptors
Journal of Computational Chemistry 33, 887-900 (2012)
Donnini, S.; Tegeler, F.; Groenhof, G.; Grubmüller, H.: Constant pH molecular dynamics in explicit solvent with lambda-dynamics. Journal of Chemical Theory and Computation 7 (6), pp. 1962 - 1978 (2011)
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